sklearn库之各分类算法简单应用

KNN

from sklearn.neighbors import KNeighborsClassifierimport numpy as npdef KNN(X,y,XX)：#X,y 分别为训练数据集的数据和标签，XX为测试数据    model = KNeighborsClassifier(n_neighbors=10)#默认为5    model.fit(X,y)    predicted = model.predict(XX)    return predicted

SVM

from sklearn.svm import SVCdef SVM(X,y,XX):    model = SVC(c=5.0)    model.fit(X,y)    predicted = model.predict(XX)    return predicted

SVM Classifier using cross validation

def svm_cross_validation(train_x, train_y):        from sklearn.grid_search import GridSearchCV        from sklearn.svm import SVC        model = SVC(kernel='rbf', probability=True)        param_grid = {'C': [1e-3, 1e-2, 1e-1, 1, 10, 100, 1000], 'gamma': [0.001, 0.0001]}        grid_search = GridSearchCV(model, param_grid, n_jobs = 1, verbose=1)        grid_search.fit(train_x, train_y)        best_parameters = grid_search.best_estimator_.get_params()        for para, val in list(best_parameters.items()):            print(para, val)        model = SVC(kernel='rbf', C=best_parameters['C'], gamma=best_parameters['gamma'], probability=True)        model.fit(train_x, train_y)        return model  

LR

from sklearn.linear_model import LogisticRegressiondef LR(X,y，XX):    model = LogisticRegression()    model.fit(X,y)    predicted = model.predict(XX)    return predicted

决策树（CART）

from sklearn.tree import DecisionTreeClassifierdef CTRA(X,y,XX):    model = DecisionTreeClassifier()    model.fit(X,y)    predicted = model.predict(XX)    return predicted

随机森林

from sklearn.ensemble import RandomForestClassifierdef CTRA(X,y,XX):    model = RandomForestClassifier()    model.fit(X,y)    predicted = model.predict(XX)    return predicted

GBDT(Gradient Boosting Decision Tree)

from sklearn.ensemble import GradientBoostingClassifier   def CTRA(X,y,XX):    model = GradientBoostingClassifier()    model.fit(X,y)    predicted = model.predict(XX)    return predicted

朴素贝叶斯：一个是基于高斯分布求概率，一个是基于多项式分布求概率，一个是基于伯努利分布求概率。

from sklearn.naive_bayes import GaussianNBfrom sklearn.naive_bayes import MultinomialNBfrom sklearn.naive_bayes import BernoulliNBdef GNB(X,y,XX):    model =GaussianNB()    model.fit(X,y)    predicted = model.predict(XX)    return predicteddef MNB(X,y,XX):    model = MultinomialNB()    model.fit(X,y)    predicted = model.predict(XX    return predicteddef BNB(X,y,XX):    model = BernoulliNB()    model.fit(X,y)    predicted = model.predict(XX    return predicted